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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Constructing neural networks with pre-specified dynamics.

Camilo J Mininni1, B Silvano Zanutto2,3

  • 1Instituto de Biología y Medicina Experimental, Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina. cmininni@fi.uba.ar.

Scientific Reports
|August 14, 2024
PubMed
Summary

Neuroscience research uses the generalized Firing-to-Parameter (gFTP) algorithm to construct binary recurrent neural networks. This method ensures network dynamics precisely match user-defined transition graphs, linking neural structure, function, and algorithms.

Keywords:
Brain dynamicsModel fittingNeural networks

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Area of Science:

  • Computational neuroscience
  • Neural network modeling
  • Systems neuroscience

Background:

  • Understanding neural population computations is key to cognitive skills.
  • Neural network models hypothesize algorithms in neural dynamics, firing statistics, and connectivity.
  • Existing methods involve parameter-defined or fitted neural networks.

Purpose of the Study:

  • Propose a method for detailed adjustment of neural network dynamics and firing statistics.
  • Link neural network dynamics, structure, and function.
  • Develop a novel algorithm for constructing binary recurrent neural networks.

Main Methods:

  • Introduce the generalized Firing-to-Parameter (gFTP) algorithm.
  • Construct binary recurrent neural networks with user-specified transition graphs.
  • Detect and modify unrealizable transition graphs while preserving information.
  • Solve linear separation problems to determine synaptic weight matrices.

Main Results:

  • gFTP successfully constructs networks with specified dynamics.
  • Demonstrated gFTP's ability to handle random, continuous attractor, and discrete attractor dynamics.
  • Showcased gFTP's utility in exploring structure-function-algorithm links.

Conclusions:

  • gFTP provides a method to build neural networks with precise dynamic control.
  • The algorithm facilitates the investigation of algorithms underlying neural computations.
  • gFTP is a valuable tool for linking neural network structure, function, and dynamics.